COPA- cancer outlier profile analysis
COPA- cancer outlier profile analysis
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DOI：
10.1093/bioinformatics/btl433
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发表时间：
2006-12-01
期刊：
影响因子：
5.8
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
--
作者：
MacDonald, James W.;Ghosh, Debashis
染色体易位在癌症中很常见，在某些情况下可能是疾病进展的原因。使用微阵列，在其中同时测量数千个基因的表达，可能会使人们能够检测到特定癌症类型的反复易位。标准的统计检验，如t-检验，不适合于检测这些易位，但可以使用基于稳健的中心和数据缩放的简单检验来帮助检测离群值样本，然后搜索具有互斥离群值的样本对，来寻找涉及反复易位的基因。我们已经在R包(我们称为COPA包)中实现了这种方法，称为癌症孤立点轮廓分析(COPA)，并展示了它在公开可用的数据集上的适用性。
Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t-test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset.